Images, such as images that may be captured by motion or still image cameras, are commonly used in a number of fields, including security, retail, entertainment, etc. For example, images may be used for detecting and tracking persons, facial recognition, flow/people-counting, quality control, and/or many other image based applications. In many instances, the images captured may be low-resolution images and may not provide enough detail for precise applications. For example, a low-resolution image may be insufficient to perform facial recognition due to the lack of detailed visual data. A challenge is how to enhance the resolution of the low-resolution image to render it suitable for appropriate operations.
One technique that has been developed to address the insufficiency of the low-resolution images involves extracting image features from a low-resolution image by applying a random forest algorithm based on a framework of the Active Shape Model (ASM) algorithm. Under this approach, feature points (e.g., facial landmarks) of the low-resolution image are adjusted using a generic ASM to fit on a real face by applying a random forest. However, this approach is not sufficiently robust to generate and/or synthesize a high-resolution image from the low-resolution image as it merely adjusts feature points of a model rather than constructing a high-resolution image.
As noted above, images may be used for flow-counting or people-counting in environments with a high volume of human traffic, such as shopping malls, railway stations, traffic intersections, etc. Flow/people-counting is important for safety considerations, such as in environments with limited capacity. Flow/people-counting is also important for marketing research, among other things. Marketing research organizations may have a high demand for analysis on the flow of consumers to determine marketing strategies. Previously, flow/people-counting and monitoring systems used infrared or thermal sensing instruments to detect and count persons. More recently, flow/people-counting systems make use of video images captured by cameras to detect and count people.
In these systems, using video data, the number of people may be counted based on the identification of either whole or certain parts of the human body. For example, in one existing approach, a camera captures a view from the top and a count line is set on the image frame. Shape, texture, and color information, is extracted from the captured images, and the information is used for identifying human heads. The number of heads passing through the count line is then counted. In another existing approach, a camera is used to detect faces in the images. Every face detected is counted as a person. However, under these approaches, interferences on the image may affect the detection of the heads and may affect the count. For example, faces in the images may be occluded or otherwise blocked, facial landmarks of the image may be incomplete, and/or light distribution in the image may affect the resolution of the image, which may prevent detection of the head and/or face. Additionally, when the interference ceases and the person re-appears, these approaches may re-detect the person and re-count the person, as there is no mechanism to determine that the person has already been counted in these approaches.
Another approach to flow/people counting proposes an algorithm to synthesize a frontal face by applying an Active Appearance Model (AAM) search and estimating an orientation angle of the face. This approach may be used in cases where a face may be incomplete because it is not frontally aligned, and is instead oriented at an angle. In this approach, the AAM orientation angle is applied to the incomplete face and the face is warped by that angle. However, this approach is inaccurate as it merely relies on the estimation of the orientation angle, and is unable to prevent duplicate counting of persons when the person re-appears after being occluded.